mlsanityData to 14 Oct 2026

Deep Learning in Medical Image Classification from MRI-based Brain Tumor Images

arXiv 2408.00636 ↗First posted 1 Aug 2024cs.CVcited by 7

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Abstract (arXiv)

Brain tumors are among the deadliest diseases in the world. Magnetic Resonance Imaging (MRI) is one of the most effective ways to detect brain tumors. Accurate detection of brain tumors based on MRI scans is critical, as it can potentially save many lives and facilitate better decision-making at the early stages of the disease. Within our paper, four different types of MRI-based images have been collected from the database: glioma tumor, no tumor, pituitary tumor, and meningioma tumor. Our study focuses on making predictions for brain tumor classification. Five models, including four pre-trained models (MobileNet, EfficientNet-B0, ResNet-18, and VGG16) and one new model, MobileNet-BT, have been proposed for this study.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 20 of its 32 references that are in the map. First posted on arXiv 1 Aug 2024.

Builds on

20 of 32 references.

References · 20

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8 more
  1. 2021Recurrent Neural Network from Adder's Perspective: Carry-lookahead RNNcited by 16
  2. 2024Utilizing Deep Learning to Optimize Software Development Processescited by 15
  3. 2024Infrared Image Super-Resolution via Lightweight Information Split Networkcited by 11
  4. 2024Utilizing Large Language Models for Information Extraction from Real Estate Transactionscited by 8
  5. 2024Application of Natural Language Processing in Financial Risk Detectioncited by 8
  6. 2024Application of Computer Deep Learning Model in Diagnosis of Pulmonary Nodulescited by 7
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  8. 2024A Survey of Retrieval Algorithms in Ad and Content Recommendation Systemscited by 5

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Cited by year

Citing papers per year: 2024: 6; 2025: 1.

7 papers.